Document Report Example

MicW2Graph

Wastewater treatment (WWT) is the process of removing contaminants from used water before it is discharged back into the environment, which contributes to address water scarcity and to protect aquatic ecosystems. In this project, we investigated the microbiome of WWT to build MicW2Graph, an open-source knowledge graph (KG) that integrates metagenomic and metatranscriptomic information with their biological context, including biological processes, environmental and phenotypic features, chemical compounds, and additional metadata. We developed a workflow to collect meta-omics datasets from MGnify and infer potential interactions among microorganisms through microbial association networks (MANs). MicW2Graph enables the investigation of research questions related to WWT, focusing on aspects such as microbial connections, community memberships, and potential ecological functions.

Abundance data

Top 5 species by biome (plotly)

import json
import plotly.io as pio

with open('../../example_data/MicW2Graph/top_species_plot_biome.json', 'r') as plot_file:
    plot_data = plot_file.read()

# Load the plot from JSON file
fig_plotly = pio.from_json(plot_data)

# Display the plot
fig_plotly.show()

Figure 1: Top 5 species by biome

Multiline plot (altair)

import altair as alt

with open('../../example_data/altair_multilineplot.json', 'r') as plot_file:
    plot_data = plot_file.read()

fig_altair = alt.Chart.from_json(plot_data)

fig_altair

Figure 2: Multiline plot

Abundance data for all studies (csv)

import pandas as pd
from itables import show
df = pd.read_csv('../../example_data/MicW2Graph/abundance_data_allbiomes.csv', delimiter=',')
show(df)

ERZ794912	ERZ794919	ERZ794932	ERZ794914	ERZ794915	ERZ794916	ERZ794917	ERZ794918	ERZ794920	ERZ794921	ERZ794930	ERZ794931	ERZ781404	ERZ781406	ERZ781405	ERZ795076	ERZ795381	ERZ764804	ERZ778038	ERZ795053	ERZ795067	ERZ795009	ERZ795010	ERZ782916	ERZ782915	ERZ782927	ERZ782929	ERZ782928	ERZ782923	ERZ782930	ERZ782925	ERZ782926	ERZ782924	ERR770579	ERR770580	ERR770581	ERR770582	ERR770583	ERR770584	ERZ1764620	ERZ1764613	ERZ1764614	ERZ1764618	ERZ1764622	ERZ1764624	ERR1726007	ERR1713394	ERR1713395	ERR1713396	ERR1713397	ERR1713398	ERR1726013	ERR1713400	ERR1726017	ERR1713402	ERR1713403	ERR1726026	ERR1713405	ERR1713406	ERR1713407	ERR1713408	ERR1713409	ERR1713410	ERR1726032	ERR1341804	ERR1341803	ERR2808645	ERR2808646	ERR2808647	ERR2808648	ERR2808649	ERR2808650	ERR2808651	ERR2808652	ERR2808653	ERR2808654	ERR2808655	ERR2808656	ERR2808657	ERR2808658	ERR2808659	ERR2808660	ERR2808661	ERR2808662	ERR2808663	ERR2808664	ERR2808665	ERR2808666	ERR2808667	ERR2808668
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Abundance data for all studies (excel)

df2 = pd.read_excel('../../example_data/MicW2Graph/abundance_data_allbiomes.xls')
show(df2)

ERZ794912	ERZ794919	ERZ794932	ERZ794914	ERZ794915	ERZ794916	ERZ794917	ERZ794918	ERZ794920	ERZ794921	ERZ794930	ERZ794931	ERZ781404	ERZ781406	ERZ781405	ERZ795076	ERZ795381	ERZ764804	ERZ778038	ERZ795053	ERZ795067	ERZ795009	ERZ795010	ERZ782916	ERZ782915	ERZ782927	ERZ782929	ERZ782928	ERZ782923	ERZ782930	ERZ782925	ERZ782926	ERZ782924	ERR770579	ERR770580	ERR770581	ERR770582	ERR770583	ERR770584	ERZ1764620	ERZ1764613	ERZ1764614	ERZ1764618	ERZ1764622	ERZ1764624	ERR2729869	ERR2729870	ERR2729871	ERR2729872	ERR2729873	ERR2729874	ERR2729875	ERR2729876	ERR2729877	ERR2729878	ERR2729879	ERR2729880	ERR2729881	ERR2729882	ERR2729883	ERR2729884	ERR2729885	ERR2729886	ERR2729887	ERR2729888	ERR2729889	ERR2729890	ERR2729891	ERR2729892	ERR2729893	ERR2729894	ERR2729895	ERR2729896	ERR2729897	ERR2729898	ERR2729899	ERR2729900	ERR2729901	ERR2729902	ERR2729903	ERR2729904	ERR2729905	ERR2729906	ERR2729907	ERR2729908	ERR2729909	ERR2729910	ERR2729911	ERR2729912	ERR2729913
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